Critical failures across all dimensions. The single tool 'execute_skill_action' has a description (Execute symbolic neural proof synthesis...) but the actual MCP server implementation in mcp_server.py is non-functional: it only prints a JSON status message and does not implement the MCP protocol at all. No tool registration, no request/response handling, no schema validation. The tool definition exists only in documentation/client code, not in the actual server. Input schema is visible (formal_conjecture, symbolic_engine) but the server never processes requests. This is a stub implementation, not a working MCP server.

• Implement the MCP protocol in mcp_server.py: add a request/response loop that reads JSON from stdin, parses tool calls, invokes the symbolic engine, and writes results to stdout.
• Register tools explicitly in the server using the MCP tools/list and tools/call handlers. Move tool definitions from client.py into the server.
• Add enum constraints to symbolic_engine parameter: e.g. enum=['SAT_SMT_NEURAL_SOLVER', 'Z3_SOLVER', 'VAMPIRE_SOLVER'] to prevent LLM hallucination.
• Document the output schema in the tool definition: specify that the response includes reasoning_job_id (string), automated_theorem_proved (boolean), formal_derivation_steps_count (integer), etc.
• Add error handling: validate formal_conjecture is non-empty, validate symbolic_engine is in the allowed set, catch solver timeouts/failures, and return structured error responses with recovery guidance (e.g., 'Solver timed out after 30s. Try a simpler conjecture or increase timeout.').